Bridging Deliberative Democracy and Deployment of Societal-Scale Technology

• URL: http://arxiv.org/abs/2303.10831v2
• Date: Mon, 27 Mar 2023 04:47:19 GMT
• Title: Bridging Deliberative Democracy and Deployment of Societal-Scale Technology
• Authors: Ned Cooper
• Abstract summary: I argue that existing processes to ensure the safety of large language models (LLMs) are insufficient and do not give the systems democratic legitimacy. This shift in AI safety research and practice will require the design of corporate and public policies that determine how to enact deliberation.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This position paper encourages the Human-Computer Interaction (HCI) community to focus on designing deliberative processes to inform and coordinate technology and policy design for large language models (LLMs) -- a `societal-scale technology'. First, I propose a definition for societal-scale technology and locate LLMs within this definition. Next, I argue that existing processes to ensure the safety of LLMs are insufficient and do not give the systems democratic legitimacy. Instead, we require processes of deliberation amongst users and other stakeholders on questions about the safety of outputs and deployment contexts. This shift in AI safety research and practice will require the design of corporate and public policies that determine how to enact deliberation and the design of interfaces and technical features to translate the outcomes of deliberation into technical development processes. To conclude, I propose roles for the HCI community to ensure deliberative processes inform technology and policy design for LLMs and other societal-scale technology.

Related papers

• Constraining Participation: Affordances of Feedback Features in Interfaces to Large Language Models [49.74265453289855]
  Large language models (LLMs) are now accessible to anyone with a computer, a web browser, and an internet connection via browser-based interfaces. This paper examines the affordances of interactive feedback features in ChatGPT's interface, analysing how they shape user input and participation in iteration.
  arXiv  Detail & Related papers  (2024-08-27T13:50:37Z)
• Position Paper: Technical Research and Talent is Needed for Effective AI Governance [0.0]
  We survey policy documents published by public-sector institutions in the EU, US, and China. We highlight specific areas of disconnect between the technical requirements necessary for enacting proposed policy actions, and the current technical state of the art. Our analysis motivates a call for tighter integration of the AI/ML research community within AI governance.
  arXiv  Detail & Related papers  (2024-06-11T06:32:28Z)
• Socialized Learning: A Survey of the Paradigm Shift for Edge Intelligence in Networked Systems [62.252355444948904]
  This paper presents the findings of a literature review on the integration of edge intelligence (EI) and socialized learning (SL) SL is a learning paradigm predicated on social principles and behaviors, aimed at amplifying the collaborative capacity and collective intelligence of agents. We elaborate on three integrated components: socialized architecture, socialized training, and socialized inference, analyzing their strengths and weaknesses.
  arXiv  Detail & Related papers  (2024-04-20T11:07:29Z)
• Addressing Social Misattributions of Large Language Models: An HCXAI-based Approach [45.74830585715129]
  We suggest extending the Social Transparency (ST) framework to address the risks of social misattributions in Large Language Models (LLMs) LLMs may lead to mismatches between designers' intentions and users' perceptions of social attributes, risking to promote emotional manipulation and dangerous behaviors. We propose enhancing the ST framework with a fifth 'W-question' to clarify the specific social attributions assigned to LLMs by its designers and users.
  arXiv  Detail & Related papers  (2024-03-26T17:02:42Z)
• The Participatory Turn in AI Design: Theoretical Foundations and the Current State of Practice [64.29355073494125]
  This article aims to ground what we dub the "participatory turn" in AI design by synthesizing existing theoretical literature on participation. We articulate empirical findings concerning the current state of participatory practice in AI design based on an analysis of recently published research and semi-structured interviews with 12 AI researchers and practitioners.
  arXiv  Detail & Related papers  (2023-10-02T05:30:42Z)
• Responsible and Inclusive Technology Framework: A Formative Framework to Promote Societal Considerations in Information Technology Contexts [1.9991645269305982]
  This paper contributes a formative framework -- the Responsible and Inclusive Technology Framework -- that orients critical reflection around the social contexts of technology creation and use. We expect that the implementation of the Responsible and Inclusive Technology framework, especially in business-to-business industry settings, will serve as a catalyst for more intentional and socially-grounded practices.
  arXiv  Detail & Related papers  (2023-02-22T18:59:04Z)
• SMART: a Technology Readiness Methodology in the Frame of the NIS Directive [0.0]
  Knowing Technology readiness level (TRL) of a given target technology proved to be useful to mitigate risks such as cost overrun, product roll out delays, or early launch failures. Originally developed for space programmes by NASA, TRL became a de facto standard among technology and manufacturing companies. We aim to address the gaps identified with existing Technology Readiness Assessment (TRA)s and aim to overcome these by developing standardised method.
  arXiv  Detail & Related papers  (2022-01-03T09:31:59Z)
• Axes for Sociotechnical Inquiry in AI Research [3.0215443986383734]
  We propose four directions for inquiry into new and evolving areas of technological development. The paper provides a lexicon for sociotechnical inquiry and illustrates it through the example of consumer drone technology.
  arXiv  Detail & Related papers  (2021-04-26T16:49:04Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Technology Readiness Levels for AI & ML [79.22051549519989]
  Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results. We propose a proven systems engineering approach for machine learning development and deployment.
  arXiv  Detail & Related papers  (2020-06-21T17:14:34Z)
• Designing for Human Rights in AI [0.0]
  AI systems can help us make evidence-driven, efficient decisions, but can also confront us with unjustified, discriminatory decisions. It is becoming evident that these technological developments are consequential to people's fundamental human rights. Technical solutions to these complex socio-ethical problems are often developed without empirical study of societal context.
  arXiv  Detail & Related papers  (2020-05-11T09:21:10Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.